Keywords

survey research, self-report data, online survey, response bias, data quality, outlier

Abstract

Online surveys are used for collecting self-report data. Despite their prevalent use, data quality problems persist due to various response biases. Here, we demonstrate how participant answering behaviors can be used to identify biased responses. We administered an online survey where participants reported their personality dimensions of neuroticism and extraversion—two personality dimensions that have been previously shown to be correlated with a propensity to deceive—and were later presented with a scenario to exhibit deceptive behavior. We then generated models to predict deception using the neuroticism and extraversion constructs. Using respondents’ fine-grained mouse movement data when answering these questions, we generated time, behavior, and navigation-based metrics to identify biased participants. By removing these outliers, model performance improved by 93% for neuroticism and 10% for extraversion. This approach aids in gaining a clearer understanding of how some types of response biases influence model performance.

Original Publication Citation

Kumar, M., Kim, D., Valacich, J. S., Jenkins, J. L., and Dennis, A. R. (2021) “Improving the Quality of Survey Data: Using Answering Behavior as an Alternative Method for Detecting Biased Respondents” Proceedings of the Twentieth Annual Workshop on HCI Research in MIS, Austin, TX December 12.

Document Type

Conference Paper

Publication Date

2021

Publisher

Proceedings of the Twentieth Annual Workshop on HCI Research in MIS

Language

English

College

Marriott School of Business

Department

Information Systems Management

University Standing at Time of Publication

Full Professor

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